Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add daodaobing/codex-project-context --skill project-contextgit clone --depth 1 https://github.com/daodaobing/codex-project-contextWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/daodaobing/codex-project-context/project-context)<a href="https://agentmods.dev/skills/daodaobing/codex-project-context/project-context"><img src="https://agentmods.dev/badge/skills/daodaobing/codex-project-context/project-context/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/daodaobing/codex-project-context/project-context"><img src="https://agentmods.dev/badge/skills/daodaobing/codex-project-context/project-context.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.01157 |
| Opus 5.5 | $0.00022 | $0.00463 |
| Sonnet 5.5 | $0.00011 | $0.00231 |
| Haiku 4.5 | $0.00006 | $0.00116 |
Grade A, and why
project-context scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Context
Use the globally registered Context MCP Server to restore task-relevant project knowledge before coding. Keep the workflow lightweight: discover candidates first, read only what the task needs, then verify against source. Never replace AGENTS.md, docs, or Serena.
When to use this skill
Consider it when the task is likely to depend on project-specific knowledge:
- unfamiliar project structure, or a repository you have not worked in yet;
- architecture, configuration, or naming conventions that must be respected;
- rules or design decisions spread across multiple docs;
- a request that is too vague to locate the right code directly;
- a change that should follow an existing design before editing.
Skip it and work directly when the task is self-contained:
- the user gives an exact file path or function name;
- a small, localized fix or a clear function-level bug;
- the unique relevant file is already known;
- a simple command or usage question;
- a general task unrelated to this repository's documentation.
Two-stage workflow
Stage 1 — Candidate discovery
When project knowledge may be needed but it is unclear which documents matter, call get_context_candidates(project_path, task). It returns metadata only (path, title, summary, role, rank, reasons) for a small set of candidate documents. This call narrows the search space; it is not a full context load and not a document read.
Stage 2 — Selective reading
Read only the candidate documents the task actually needs, judged from the task plus each candidate's path, title, summary, role, and reasons. Reading one or two documents is normal; reading every candidate is not required. If the metadata already gives enough direction, continue straight to source verification.
Source and tests remain the final truth. Documents narrow the search and restore design context, but always confirm behavior against the current code.
Decision model
- Do I already have enough context? (exact file or function given, small change in known code) -> work directly.
- Does the task depend on project-specific knowledge rather than pure code logic? No -> work directly.
- Do I already know exactly which document or file to read? Yes -> read it directly.
- Otherwise ->
get_context_candidates(project_path, task), read only the chosen candidates, then verify in source and tests.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 87 lines · 55 tokens per session scan A 4291dd3493f9
project-context is a skill published in the GitHub repository daodaobing/codex-project-context (1 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 55 tokens to every session and 1,157 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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